title: High-throughput methods for combinatorial drug discovery.
abstract: A more nuanced approach to drug design is to use multiple drugs in combination to target interacting or complementary pathways. Drug combination treatments have shown higher efficacy, fewer side effects, and less toxicity compared to single-drug treatment. In this Review, we focus on the use of high-throughput biological measurements (genetics, transcripts, and chemogenetic interactions) and the computational methods they necessitate to further combinatorial drug design (CDD). We highlight the state-of-the-art analytical methods, including network analysis, integrative informatics, and dynamic molecular modeling, that have been used successfully in CDD. Finally, we present an exhaustive list of the publicly available data and methodological resources available to the community. Such next-generation technologies that enable the measurement of millions of cellular data points simultaneously may usher in a new paradigm in drug discovery, where medicine is viewed as a system of interacting genes and pathways rather than the result of an individual protein or gene.
authors.affiliation: Department of Biomedical Informatics, Columbia University, New York, NY 10032, USA.
authors.forename: Xiaochen
authors.initials: X
authors.lastname: Sun
authors.forename: Santiago
authors.initials: S
authors.lastname: Vilar
authors.forename: Nicholas P
authors.initials: NP
authors.lastname: Tatonetti
mesh_terms: D000818:Animals; D020650:Combinatorial Chemistry Techniques / Q000379:methods*; D019295:Computational Biology; D055808:Drug Discovery; D004359:Drug Therapy, Combination; D057166:High-Throughput Screening Assays / Q000379:methods*; D006801:Humans; D049490:Systems Biology